Fracture Prediction and Correlation of AlSi Hot …/media/Files/Autosteel/Great...Fracture...

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Fracture Prediction and Correlation of AlSi Hot Stamped

Steels with Different Models in LS DYNA

Gang Huang, Sriram Sadagopan, Hong Zhu, Min Kuo

ArcelorMittal

Yijung Chen, Cedric Xia, Omar Faruque

Ford Motor Company

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Outline

• Introduction

• Selected fracture criteria in LS DYNA

• Calibration tests

– Determination of fracture strains

• FE simulations and parameter identification for

fracture criteria

• Model validation

– Test samples

– Components

• Conclusions

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Introduction

• Overview

– Lightweighting of body structures to meet upcoming CAFÉ standards of 54.5 Mpg by 2025 while meeting stringent safety standards

– Use of CAE is a critical step in the design process

•Critical to ensure prediction accuracy when modeling crash loading

– ArcelorMittal’s AlSi coated hot stamping product, USIBOR® provides an excellent combination of manufacturability and performance

• Project Scope

– Characterize fracture behavior of USIBOR®

– Calibrate recently developed fracture models

– Use of fracture models in addition to computationally advanced CAE techniques to improve simulation accuracy

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Fracture Criteria: Some Definitions

m

Stress triaxiality

6 21 1 cosarc

3

cos(3 )r

1 , 1

1 2 3

2 2 2

1 2 2 3 3 1

1/3

1 2 3

1( )

3

1( ) ( ) ( )

2

27( )( )( )

2

m

m m mr

Hydrostatic mean stress

von Mises stress

The third deviatoric stress invariant

Lode Angle

The normalized third stress invariant

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Selected Fracture Criteria in LS DYNA

• Three fracture models implemented in LS DYNA are selected for fracture prediction

– MIT MMC Model

•Closed form solution for fracture strain as a function of the triaxiality factor and Lode angle – Implemented as discrete point input

– GISSMO Model

•Discrete points for fracture strain as a function of triaxiality factor and Lode angle

•Damage parameter included in calculation of stresses during solution process

– Modified Johnson-Cook Model

•Discrete points for fracture strain as a function of triaxiality factor

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Selected Fracture Criteria in LS DYNA

• MIT MMC Model

• GISSMO Model

• Modified Johnson Cook Model

n

ss

f ff

CCC

K

1

2

6sin

3

1

6cos

3

11

6sec1

32

3

n

f

pD

pwp

n

n

f

dfdDn

dD

1

),(

,)(1

ppww dfD

f

ppww dfD

f

)(1

3

21

C

f eCC

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Cut (a) Uniaxial (b) out test (c) Plane strain (d) Biaxial stretch (e) Equibiaxial

Calibration Tests

• Necessary for determination of fracture strains and

the stress state variables

• DIC for strain measurements

• FEA for stress history

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Calibration Tests

• Uniaxial tension and cut-out tests

• Strain evolution and fracture strain measured by DIC

Test set-up Uniaxial tension Cut-out test

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Calibration Tests

• Plane strain, biaxial and equibiaxial stretch

Test set-up

cameras

MTS LDH machine

Plane strain

Equibiaxial

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Calibration Tests: Fracture strains

Major Minor Effective strain

Test 1-L 0.381 -0.238 0.385

Test 2-L 0.402 -0.232 0.404

Test 3-L 0.415 -0.259 0.419

Average 0.399 -0.243 0.403

Test 1-T 0.355 -0.228 0.360

Test 2-T 0.335 -0.212 0.339

Test 3-T 0.326 -0.241 0.338

Average 0.339 -0.227 0.346

Major strain Minor strain Effective strain

Test 1 0.183 -0.034 0.195

Test 2 0.189 -0.041 0.199

Test 3 0.2168 -0.047 0.228

Average 0.196 -0.041 0.207

Uniaxial tension Cut out test

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Calibration Tests: Fracture strains

Major strain Minor strain Thinning Effective Original

thickness Final

thickness Measured thinning

Test 1 0.134 0.105 -0.239 0.240 1.594 1.250 -0.243

Test 2 0.152 0.133 -0.285 0.285 1.580 1.226 -0.254

Test 3 0.129 0.11 -0.239 0.239 1.594 1.248 -0.245

Average 0.138 0.116 -0.254 0.255 1.589 1.241 -0.247

Equibiaxial

Major strain Minor strain Effective

Test 1 0.115 0.003 0.134

Test 2 0.114 0.011 0.139

Test 3 0.101 0.013 0.125

Average 0.118 0.009 0.133

Plane strain

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Calibration Tests: Stress State History

12

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

0 0.3 0.6 0.9 1.2 1.5

Load

(N)

Displacement (mm)

Load-displacement curve - cut-out sample

Experiment

Simulation

Cut out test

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 0.1 0.2 0.3 0.4

Tria

xial

ity/

Lod

e a

ngl

e

Effective plastic strain

Lode parameter

Triaxiality

Sample with cut-outStress state variables – cut out test

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0

50

100

150

200

250

300

0 5 10 15 20 25 30

Load

(kN

)

Displacement (mm)

Load-displacement curves - Equibiaxial

Experiment -1

Experiment -2

Experiment -3

Simulation

Calibration Tests: Stress State History

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

0 0.1 0.2 0.3 0.4 0.5Tr

iaxi

alit

y/Lo

de

an

gle

Effective plastic strain

Lode parameter

Triaxiality

EquibiaxialStress state variables – equibiaxial test

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Parameter Identification for Models

• Use of MATLAB to determine parameters for the

fracture model

Triaxiality Lode angle Fracture strain

Uniaxial 0.379 0.839 0.346

Cut-out 0.537 0.295 0.207

Plane strain 0.565 0.040 0.133

Biaxial 0.645 -0.870 0.232

Equi-biaxial 0.662 -0.999 0.255

Data points for MIT MMC model parameter identification

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Calibration Results: MIT MMC Model

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Calibration Results – GISSMO model

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Calibration Results –Johnson-Cook Model

17

0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

triaxility

fractu

re s

train

Modified Johnson-Cook model

Experimental data

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0

5

10

15

20

25

30

35

0 1 2 3 4

Load

(KN

)

Displacement (mm)

Uniaxial Test

Experiment

CAE-GISSMO

CAE-MIT MMC

CAE-no fracture criterion

Model Validation: Test Samples

• Use of MIT-MMC and GISSMO models yield very

accurate predictions of failure

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Model Validation: Test Samples

• Use of MIT-MMC and GISSMO models yield very

accurate predictions of failure

0

5

10

15

20

25

30

35

40

45

50

0 0.5 1 1.5Lo

ad (K

N)

Displacement (mm)

Cut-out sample

Experiment

CAE-GISSMO

CAE-MIT MMC

CAE-no fracture criterion

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Model Validation: Components

• Sled tests

• Very fine model (100 M solid elements) for the B pillar

• 8 elements through the thickness

• Incorporation of weld properties in the simulation

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Model Validation: Components

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Model Validation: Components

• CAE predictions correlate very well to component test results

• Factors responsible for very good correlation

– Very fine mesh with solid elements

– Inclusion of damage in the simulations

– Incorporation of spot weld properties and HAZ in the simulation

• However computational resources required are very high

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Conclusions

• Five tests with different stress states were used in

calibration of the 3 models in LS DYNA

• FEA simulation with implemented fracture models

performed on the calibration tests to verify

applicability of fracture models

• Incorporation of damage and spot weld properties

are very important for accurate predictions of

fracture behavior in component level testing